1. Cyclone composites in the real world and ACCESS Pallavi Govekar, Christian Jakob, Michael Reeder and Jennifer Catto

2. Introduction The energy budget of the southern hemisphere is poorly represented in nearly all climate models (Trenberth and Fasullo, 2010), with potential impacts on SST simulations. The Southern Ocean storm tracks and their associated baroclinic systems (Simmonds and Keay, 2000, Trenberth, 1991) and fronts (Berry et al., 2011) are the paramount dynamical features of the Southern Hemisphere. Cloudiness over the southern hemisphere is difficult to study as there are few in-situ observations. We have to rely on satellite products. The 2B-GEOPROF- LIDAR product which combines the Cloud Profile Radar (CPR) data from the CloudSat satellite with the CALIOP lidar data from the CALIPSO satellite is used. Cloudsat observed radar reflectivity, adapted from Posselts et al., 2008.

3. Data MAP climatology of Mid-latitude Storminess (MCMS) Provides track, age, area, depth, grid SLP, regional SLP, intensity of the cyclone. National Centers for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR) Reanalysis Three hourly and daily datasets for mean sea level pressure (MSLP), potential temperature, vertical velocity on 2.5 degree grid.

4. Method to Composite Cyclones 1. Identify and select cyclone tracks 2. Find the position where cyclone satisfies given criterion. 3. Draw a 4000 km x 4000 km grid box around each point. 4. Overlay all boxes on each other in the new defined co-ordinate system. 5. For cloud fraction: The cyclone area box is subdivided into 100 km x100 km boxes. The cloudy and total pixels falling into each box are counted to give the cloud fraction in each 240-m height bin.

5. Model simulation against CloudSat observations To compare model simulations to CloudSat/ CALIPSO observations, the Australian Community Climate and Earth System Simulator (ACCESS) model has been run for 2 years (2000- 2001) using model version 7.1 The model data is available on 1.88 x 1.25 grid on 38 vertical levels for every 6- hour. Bulk cloud fraction field from model was extracted without using any simulator. To better match the observations, CloudSat-CALIPSO observations were averaged according to the thickness of model levels. As CloudSat-CALIPSO dataset includes precipitation in the cloud mask, two set of observations are considered. First includes the total cloud fraction which is considered as an upper bound for observations and later includes cloud fraction only when there was no precipitation detected at the ground and is considered as lower bound for observations.

6. Cloud cover form CloudSat Cloudsat - upper boundCloudsat - lower boundDifference 1 km < h < 2 km 4 km < h < 5 km 9 km < h < 10km

7. Cross-sections taken along the lines CD AB

8. Cloud cover from a model simulation Model Model-upper boundModel-lower bound 4 km < h < 5 km 9 km < h < 10km 1 km < h < 2 km

9. Cross-sections taken along the lines CD AB

10. Relationship between dynamical fields and clouds

11. Summary The overall cloud structure is well captured by the model. In the model, high level cloud occurrence is overestimated while low level cloud occurrence is underestimated. There are too little clouds behind the system and too much high clouds in the warm frontal region. All the dynamical fields agrees well with cloud structure around extratropical cyclones. The range of most dynamical variables in the composite cyclone is smaller than observed, indicating that the dynamical properties of the model cyclones are not well simulated.

12. Thank you!